52 citations · 295 across the 56 of their papers we have counts for
4 papers · 1 filter
MATCHA: Efficient Deployment of Deep Neural Networks on Multi-Accelerator Heterogeneous Edge SoCs
Enrico Russo, Mohamed Amine Hamdi, Alessandro Ottaviano +6
Deploying DNNs on System-on-Chips (SoC) with multiple heterogeneous acceleration engines is challenging, and the majority of deployment frameworks cannot fully exploit heterogeneit…
MATCH: Model-Aware TVM-based Compilation for Heterogeneous Edge Devices
Mohamed Amine Hamdi, Francesco Daghero, Giuseppe Maria Sarda +6
Streamlining the deployment of Deep Neural Networks (DNNs) on heterogeneous edge platforms, coupling within the same micro-controller unit (MCU) instruction processors and hardware…
Optimizing Foundation Model Inference on a Many-tiny-core Open-source RISC-V Platform
Viviane Potocnik, Luca Colagrande, Tim Fischer +4
Transformer-based foundation models have become crucial for various domains, most notably natural language processing (NLP) or computer vision (CV). These models are predominantly…
DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs
Alessio Burrello, Angelo Garofalo, Nazareno Bruschi +3
The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applicati…